Advanced Micro Devices Corporation

Ati Technologies Ulc

Furkan Eris

Paul S Keltcher

Alok Garg

Mayank Chhablani

John Kalamatianos

Bradford Michael Beckmann

Sergey Blagodurov

Khaled Hamidouche

Kishore Punniyamurthy

Brandon K Potter

Richard David Sodke

Techniques are described for a hardware processor to dynamically configure a component that improves a processor function with a configuration setting based on invariant statistics. The invariant statistics are generated by execution of the instructions from one or more applications and are independent of the performance metrics of the processor function for the execution. In an embodiment, the configuration setting for the component is generated using a machine learning model.

Invariant statistics-based configuration of processor components

A device includes a plurality of processing elements (PEs). A symmetric memory is allocated in each of the plurality of PEs. The device includes a switch connected to the plurality of PEs. The switch is to: receive, from a first processing element (PE) of the plurality of PEs, a message that includes a buffer offset, compute, based on the buffer offset, a first memory address of a first buffer in a first symmetric memory of the first PE and a second memory address of a second buffer in a second symmetric memory of a second PE of the plurality of PEs, and initiate, based on the first memory address and the second memory address, a direct memory access operation to access the first buffer and the second buffer.

Low latency offloading of collectives over a switch

Techniques for identifying a hardware configuration for operation are disclosed. The techniques include applying feature measurements to a trained model; obtaining output values from the trained model, the output values corresponding to different hardware configurations; and operating according to the output values, wherein the output values include one of a certainty score, a ranking, or a regression value.

Hardware configuration selection using machine learning model

Techniques for generating a model for predicting when different hybrid prefetcher configurations should be used are disclosed. Techniques for using the model to predict when different hybrid prefetcher configurations should be used are also disclosed. The techniques for generating the model include obtaining a set of input data, and generating trees based on the training data. Each tree is associated with a different hybrid prefetcher configuration and the trees output certainty scores for the associated hybrid prefetcher configuration based on hardware feature measurements. To decide on a hybrid prefetcher configuration to use, a prefetcher traverses multiple trees to obtain certainty scores for different hybrid prefetcher configurations and identifies a hybrid prefetcher configuration to used based on a comparison of the certainty scores.

Multi-class multi-label classification using clustered singular decision trees for hardware adaptation

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Boston, MA, United States of America

Furkan Eris